Load data
Set up the experiment-specific parameters
numItemsInStream<- length( data$letterSeq[1,] )
minSPE<- -17; maxSPE<- 17
annotateIt<-TRUE
numSs<-length(unique(df$subject))
cat( paste0('Total num Ss=',numSs) )
## Total num Ss=80
Plot sample histograms, make sure plotting working.
dg<- df %>% filter(subject < "AC")
library(ggplot2)
g=ggplot(dg, aes(x=SPE)) + facet_grid(subject+orientation~stream) #, scales="free_y")
g<-g+geom_histogram(binwidth=1,color="grey90") + xlim(minSPE,maxSPE)
g<-g+ geom_text(x=12, y= 33, aes(label = subject)) #inset subject name/number. Unfortunately it overwrites itself a million times
show(g)
## Warning: Removed 144 rows containing non-finite values (stat_bin).
Plot all Ss
g=ggplot(df, aes(x=SPE)) + facet_grid(subject+orientation~stream) #, scales="free_y")
g<-g+geom_histogram(binwidth=1,color="grey90") + xlim(minSPE,maxSPE)
g<-g+ geom_text(x=12, y= 33, aes(label = subject)) #inset subject name/number. Unfortunately it overwrites itself a million times
show(g)
## Warning: Removed 5760 rows containing non-finite values (stat_bin).
CY is bimodal but not outrageously so. SsWhoLookWeird<-c(“CY”,
Ultimately want to analyze data broken down by: * Group (determines in which serial positions the targets appeared) * Condition (3-day, 2-day, 1-day) * targetSP (formerly known as TargPos (target position this trial)) * Block (epochs of trials basically) * Subject
condtnVariableNames <- c("Group","Condition","targetSP","Subject","Epoch")